Metadata Reporting Standards
RNA Sequencing (RNA-seq)
RNA-seq is a high-throughput sequencing-based assay that quantifies the abundance of RNA transcripts within a biological sample to characterize gene expression. In bulk RNA-seq, the average gene expression profile of a heterogeneous cell population is measured; in single-cell RNA-seq (scRNA-seq), unique molecular identifiers (UMIs) and per-cell barcodes are applied during library preparation to resolve gene expression at single-cell resolution. RNA-seq enables comprehensive, untargeted profiling of the transcriptome.
DOI: https://doi.org/10.35079/HBM556.VHXG.898
Published: September 2, 2026
Schema
Legend:
Text field
Allowable value
Radio
Numeric
* Required
| Attribute | Type | Description | Allowable Values |
|---|---|---|---|
| Parent sample ID * | The unique identifier from HuBMAP or SenNet for the sample (such as a block, section, or suspension) used to perform the assay. For instance, in an RNAseq assay, the parent sample would be the suspension, while in imaging assays, it would be the tissue section. If the assay is derived from multiple parent samples, this field should contain a comma-separated list of identifiers. Example: HBM386.ZGKG.235, HBM672.MKPK.442 | ||
| Lab ID | An identifier assigned by the data provider to reference an external metadata record for the dataset. The external record may be maintained independently and can support dataset traceability and provenance tracking. Leave this field empty if no such identifier exists. | ||
| Preparation protocol DOI * | The DOI for the protocols.io page that details the assay or the procedures used for sample procurement and preparation. For example, in the case of an imaging assay, the protocol may start with tissue section staining and end with the generation of an OME-TIFF file. The documented protocol should also include any image processing steps involved in producing the final OME-TIFF. Example: https://dx.doi.org/10.17504/protocols.io.eq2lyno9qvx9/v1 | ||
| Dataset type * | The specific type of dataset being produced. Example: RNAseq | HiFi-Slide SNARE-seq2 COMET Visium (no probes) DESI Confocal Stereo-seq Visium (with probes) Molecular Cartography Virtual Histology DBiT-seq Seq-Scope CosMx Transcriptomics CyCIF Light Sheet iCLAP seqFISH ATACseq CosMx Proteomics Singular Genomics G4X Visium HD MERFISH 10X Multiome 4i PhenoCycler Second Harmonic Generation (SHG) Thick section Multiphoton MxIF MACSima CyTOF Olink MIBI Auto-fluorescence FACS Xenium DNA Methylation SIMS Cell DIVE CODEX GeoMx (NGS) MUSIC Pixel-seqV2 MALDI 2D Imaging Mass Cytometry Histology Enhanced Stimulated Raman Spectroscopy (SRS) Illumina Spatial ver0 DART-FISH Resolve RNAseq LC-MS nanoSPLITS GeoMx (nCounter) Raman Imaging RNAseq (with probes) Paired-Tag MS Lipidomics STARmap MPLEx |
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| Analyte class * | The analyte class which is the target molecule that the assay is measuring. Example: DNA | DNA + RNA Nucleic acid + protein Chromatin Lipid + metabolite + protein RNA + protein RNA Metabolite Unsaturated lipid Lipid + metabolite Saturated lipid Lipid Protein Fluorochrome Collagen DNA Peptide Polysaccharide Endogenous fluorophore |
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| Is targeted? * | Indicates whether a specific molecule or set of molecules is targeted for detection or measurement by the assay. Example: Yes | Yes No |
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| Acquisition instrument vendor * | The company that manufactures or supplies the acquisition instrument. An acquisition instrument is a device equipped with signal detection hardware and signal processing software. It captures signals produced by assays, such as variations in light intensity or color, or signals corresponding to molecular mass. If the instrument was custom-built or developed internally, enter “In-House”. Example: Illumina | BGI Genomics Cytiva Thermo Fisher Scientific Zeiss Microscopy Complete Genomics 3DHISTECH GE Healthcare Leica Microsystems Akoya Biosciences NanoString Element Biosciences Andor Huron Digital Pathology Illumina Ionpath Waters In-House Resolve Biosciences Singular Genomics Vizgen Standard BioTools (Fluidigm) Sciex Bruker Evident Scientific (Olympus) Keyence Miltenyi Biotec Leica Biosystems Revvity Cytek Biosciences 10x Genomics Microscopes International Hamamatsu Motic |
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| Acquisition instrument model * | The specific model of the acquisition instrument, as manufacturers often offer various versions with differing features or sensitivities. These differences may be relevant to the processing or interpretation of the data. If the instrument was custom-built or developed internally, enter “In-House”. If the model is unknown, enter “Unknown”. Example: HiSeq 4000 | SCN400 STELLARIS 5 BZ-X710 Pannoramic MIDI II Digital Scanner Not applicable MoticEasyScan One EVOS M7000 Orbitrap Astral NovaSeq X LSM 710 Confocal Microscope NanoZoomer 2.0-HT timsTOF Ultra 2 Lightsheet 7 Phenocycler-Fusion 1.0 DNBSEQ-T7 timsTOF Pro Unknown AVITI DMi8 Opera Phenix Plus HCS timsTOF Pro 2 Q Exactive UHMR Q Exactive timsTOF SCP Zyla 4.2 sCMOS Helios uScopeHXII-20 Orbitrap Fusion Tribrid Custom: Multiphoton QTRAP 5500 timsTOF Ultra BZ-X800 CyTOF 2 G4X Spatial Sequencer NextSeq 500 NanoZoomer S360 Hyperion Imaging System NovaSeq X Plus CyTOF XT NanoZoomer-SQ NextSeq 550 Axio Zoom.V16 Digital Spatial Profiler timsTOF FleX timsTOF FleX MALDI-2 NanoZoomer S210 BZ-X810 Axio Observer 7 Cytek Northern Lights iScan System Opera Phenix HCS Zeiss LightSheet Z.1 IN Cell Analyzer 2200 timsTOF HT PhenoImager Fusion DM6 B Phenocycler-Fusion 2.0 Aperio CS2 Orbitrap Fusion Lumos Tribrid Resolve Biosciences Molecular Cartography MALDI timsTOF Flex Prototype TissueScope LE Slide Scanner VS200 Slide Scanner Axio Observer 5 NanoZoomer 2.0-RS Axio Observer 3 HiSeq 2500 Orbitrap Eclipse Tribrid Cell DIVE MERSCOPE NextSeq 2000 NovaSeq 6000 In-House HiSeq 4000 Q Exactive HF-X solariX Panoramic 150 Digital Scanner Aperio AT2 MIBIscope SYNAPT G2-Si MACSima System Biomark HD NanoZoomer S60 CosMx Spatial Molecular Imager MERSCOPE Ultra Axio Scan.Z1 Juno System Q Exactive HF Xenium Analyzer |
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| Source storage duration value * | The length of time the sample was stored prior to processing it. For assays performed on tissue sections, this refers to how long the tissue section (e.g., slide) was stored before the assay began (e.g., imaging). For assays performed on suspensions, such as sequencing, it refers to how long the suspension was stored before library construction started. Example: 12 | ||
| Source storage duration unit * | The unit of measurement used to specify the source storage duration value. Example: hour | hour month year day minute |
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| Time since acquisition instrument calibration value | The length of time since the acquisition instrument was last serviced or calibrated. This provides a metric for assessing drift in data capture. Example: 10 | ||
| Time since acquisition instrument calibration unit | The unit of measurement used to specify the time since acquisition instrument calibration value. Example: month | month year day |
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| Contributors path * | The name of the file containing the ORCID IDs for all contributors to this dataset. Example: ./contributors.csv | ||
| Data path * | The top-level directory containing the raw and/or processed data. For a single dataset upload, this might be represented as “.”, whereas for a data upload containing multiple datasets, this would be the directory name for the respective dataset. For example, if the data is within a directory named “TEST001-RK”, use the syntax “./TEST001-RK” for this field. If there are multiple directory levels, use the format “./TEST001-RK/Run1/Pass2”, where “Pass2” is the subdirectory where the single dataset’s data is stored. This is an internal metadata field used solely for data ingestion. Example: ./TEST001-RK | ||
| Barcode offset * | The position within a sequencing read where a barcode sequence begins. In sequencing libraries, barcodes—short DNA sequences used to tag individual cells or molecules—are often arranged in a pattern where each barcode is separated by a constant sequence of known length. These constant sequences create predictable spacing between barcodes, known as offsets. For example, if barcodes are 8 base pairs (bp) long and separated by 30 bp constant regions, the barcodes might start at positions 0, 38, and 76 within the read. If the source material is not barcoded, this field should be marked as “Not applicable”. Example: 0,38,76 | 1 8 20 0,14 1,27 0,38,76 10,48,78 10,48,86 0,20-23,51-54 Not applicable |
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| Barcode read * | The sequencing read file contains the cell or capture spot barcode sequences. This information is essential when constructing sequencing libraries with a non-commercial kit, as it ensures that barcodes are correctly extracted during data processing. If the source material is not barcoded, this field should be marked as “Not applicable”. Example: Read 1 (R1) | Read 1 (R1) Not applicable Read 2 (R2) |
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| Barcode size * | The length of each cell or capture spot barcode in base pairs (bp). If the source material is not barcoded, enter “Not applicable”. Example: 16 | 14 16 40 8,6 10,10 8,8,8 14-17,14,14 Not applicable |
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| UMI offset * | The position within a sequencing read where the Unique Molecular Identifier (UMI) sequence begins. UMIs are short, random nucleotide sequences added to DNA or RNA fragments during library preparation to uniquely tag each original molecule. If no UMI is used, enter “Not applicable”. Example: 16 | 16 36 Not applicable |
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| UMI read * | Specifies which sequencing read file contains the Unique Molecular Identifier (UMI) barcode. If no UMI is used, enter “Not applicable”. Example: Read 1 (R1) | Read 1 (R1) Not applicable Read 2 (R2) |
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| UMI size * | The length of the Unique Molecular Identifier (UMI) in base pairs (bp). If no UMI is used, enter “Not applicable”. Example: 8 | 8 9 10 12 Not applicable |
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| Assay input entity * | The entity from which the analyte is captured. For example, in bulk sequencing, the source entity would be “tissue” while in single-cell sequencing, it would be “single cell”. Example: single cell | single cell tissue (bulk) spot single nucleus area of interest |
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| Number of input cells or nuclei | The number of cells or nuclei that were input into the assay. This information is typically not available for assays performed on bulk tissue preparations, and therefore this field may be left blank. Example: 1000 | ||
| Amount of input analyte value | The amount of RNA or DNA input into the assay, typically measured using instruments such as a Qubit, BioAnalyzer, or TapeStation. In most single-cell or single-nucleus assays, this value is usually not available, and therfore this field may be left blank. Example: 500 | ||
| Amount of input analyte unit | The unit of measurement used to specify the amount of input analyte value. If no input amount, this field may be left blank. Example: ng | ug ng |
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| Library adapter sequence * | The 5’ and/or 3’ adapter sequences used during library preparation to make the library compatible with the sequencing protocol and instrumentation. Adapter sequences should be provided as a comma-separated list of key-value pairs in the format: “adapter name: sequence”. Example: Nextera Read 1: CTGTCTCTTATACACATCT | ||
| Library average fragment size * | The average size of sequencing library fragments, measured in base pairs (bp), as estimated by gel electrophoresis, BioAnalyzer, or TapeStation. Example: 200 | ||
| Library input amount value * | The amount of cDNA, after amplification, that was used for library construction. Example: 60 | ||
| Library input amount unit * | The unit of measurement used to specify the library input amount value. Example: ng | ul ng |
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| Library output amount value | The total amount of library following the clean-up step after the final PCR amplification. To calculate, multiply the Qubit-measured concentration (ng/µl) by the elution volume (µl) obtained after the final clean-up. Example: 125 | ||
| Library output amount unit | The unit of measurement used to specify the library output amount value. Example: ng | ul ng |
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| Library concentration value * | The concentration of the pooled library submitted for sequencing. Example: 2.5 | ||
| Library concentration unit * | Unit of library concentration value. | ng/ul pg/ul nM |
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| Library layout * | Indicates whether the sequencing library was prepared for single-end or paired-end sequencing. This information determines how reads are generated and aligned during data processing. Example: single-end | single-end paired-end |
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| Number of iterations of cDNA amplification * | The number of amplification cycles applied to cDNA prior to library construction. This is typically the number of PCR cycles used. For protocols using linear amplification methods (e.g., aRNA), this refers to the number of amplification rounds performed. Example: 3 | ||
| Number of PCR cycles for indexing * | The number of PCR cycles used specifically to add sequencing adapters and amplify the library during the indexing step. This count does not include earlier cDNA amplification cycles, which are recorded separately under the “Number of iterations of cDNA amplification” field. Example: 8 | ||
| Library preparation kit * | The reagent kit used to construct the sequencing library, including both the kit name and, if available, version and product number. If the library was prepared using a custom protocol, enter “Custom”. Example: 10X Genomics; Chromium Next GEM Single Cell 5’ Kit v2, 16 rxns; PN 1000263 | 10X Genomics; Chromium Next GEM Single Cell 3' HT Kit v3.1, 8 rxns; PN 1000370 10X Genomics; Chromium Next GEM Automated Single Cell 5' Kit v2, 4 rxns; PN 1000298 10x Genomics; Chromium Next GEM Single Cell Fixed RNA Sample Preparation Kit, 16 rxns; PN 1000414 10X Genomics; Visium Spatial Gene Expression Slide and Reagent Kit, 4 slides, 16 reactions; PN 1000184 Parse Biosciences; Evercode WT v2 Kit, 48 rxns; PN ECW02030 10X Genomics; Chromium Next GEM Single Cell 5' Kit v2, 16 rxns; PN 1000263 10X Genomics; Visium Spatial for FFPE Gene Expression Kit, Human Transcriptome, 1 slides, 4 reactions; PN 1000338 10X Genomics; Chromium Next GEM Single Cell 3' Kit v3.1, 16 rxns; PN 1000268 10x Genomics; Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit, 16 rxns; PN 1000175 10x Genomics; Library Construction Kit C, 16 rxns; PN 1000694 10x Genomics; Visium CytAssist Spatial Gene Expression for FFPE, Mouse Transcriptome, 6.5mm, 4 rxns; PN 1000521 10X Genomics; Chromium Next GEM Single Cell 3' HT Kit v3.1, 48 rxns; PN 1000348 10X Genomics; Chromium Next GEM Single Cell 3' GEM, Library & Gel Bead Kit v3.1, 16 rxns; PN 1000121 10x Genomics; Library Construction Kit C, 4 rxns; PN 1000689 10X Genomics; Visium CytAssist Spatial Gene Expression for FFPE, Human Transcriptome, 11 mm, 2 reactions; PN 1000522 Illumina; TruSeq Stranded mRNA Library Prep (48 samples); PN 20020594 10X Genomics; Automated Library Construction Kit, 24 rxns; PN 1000428 New England BioLabs; NEBNext Ultra II RNA Library Prep Kit for Illumina; PN E7770 10X Genomics; Chromium Next GEM Automated Single Cell 5' Kit v2, 24 rxns; PN 1000290 10X Genomics; Visium Spatial Gene Expression Slide and Reagent Kit, 1 slides, 4 reactions; PN 1000187 10x Genomics; Chromium GEM-X Single Cell 5' Kit v3, 16 rxns; PN 1000699 10x Genomics; Chromium Single Cell ATAC Library & Gel Bead Kit, 16 rxns; PN 1000110 10x Genomics; Library Construction Kit, 16 rxns; PN 1000190 Parse Biosciences; Evercode WT Mini v2 Kit, 12 rxns; PN ECW02010 10x Genomics; Chromium Next GEM Single Cell 5' HT Kit v2, 48 rxns; PN 1000356 Illumina; TruSeq Stranded mRNA Library Prep (96 samples); PN 20020595 10X Genomics; Chromium Next GEM Single Cell 5' Kit v2, 4 rxns; PN 1000265 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 4 rxn; PN 1000285 10X Genomics; Chromium Next GEM Single Cell Fixed RNA Hybridization & Library Kit, 4 rxns; PN 1000415 Custom 10X Genomics; Chromium Single Cell 3' GEM, Library & Gel Bead Kit v3, 4 rxns; PN 1000092 10x Genomics; Chromium Single Cell 3ʹ GEM, Library & Gel Bead Kit v3, 16 rxns; PN 1000075 10X Genomics; Chromium Single Cell 3' Library & Gel Bead Kit, 4 rxns; PN 120267 10X Genomics; Visium Spatial for FFPE Gene Expression Kit, Mouse Transcriptome, 4 rxns; PN 1000339 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 16 rxn; PN 1000283 Epigenome Technologies; Multiplex Droplet Paired-Tag 16 Reaction Kit; PN MDP16101 10X Genomics; Chromium Next GEM Single Cell 3' Kit v3.1, 4 rxns; PN 1000269 10x Genomics; GEM-X Flex GEM & Library Kit, 4 rxns; PN 1000782 10x Genomics; Chromium Next GEM Single Cell 5' HT Kit v2, 8 rxns; PN 1000374 10X Genomics; Visium CytAssist Spatial Gene Expression for FFPE, Human Transcriptome, 6.5mm, 4 reactions; PN 1000520 |
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| Sample indexing kit * | The reagent kit used to add index sequences to samples. Indexes enable multiplexing, allowing multiple libraries to be pooled and sequenced together on the same Illumina flow cell. Each indexing kit includes a set of compatible index sequences (often called “indexing sets”) used to uniquely label a specific number of samples, depending on the kit’s design. If the library was prepared using a custom protocol, enter “Custom”. If no indexing was performed, enter “Not applicable”. Example: 10X Genomics; Dual Index Kit TS, Set A; PN 1000251 | 10x Genomics; Chromium i7 Multiplex Kit, 96 rxns; PN 120262 Illumina; TruSeq RNA CD Index Plate (96 Indexes, 96 Samples); PN 20019792 Not applicable Illumina; TruSeq RNA Single Indexes Set A (12 Indexes, 48 Samples); PN 20020492 Illumina; TruSeq RNA Single Indexes Set B (12 Indexes, 48 Samples); PN 20020493 10X Genomics; Chromium i7 Sample Index Plate (96 rxn); PN 220103 NanoString Technologies; GeoMx Seq Code Pack; PN GMX-NGS-SEQ-AB NanoString Technologies; GeoMx Seq Code Pack; PN GMX-NGS-SEQ-CD NanoString Technologies; GeoMx Seq Code Pack; PN GMX-NGS-SEQ-EF NanoString Technologies; GeoMx Seq Code Pack; PN GMX-NGS-SEQ-GH Custom 10x Genomics; Chromium i7 Multiplex Kit N, Set A, 96 rxn; PN 1000084 Parse Biosciences; Fragmentation Reagents; PN WX100 Integrated DNA Technologies: Custom DNA Oligos Parse Biosciences; UDI Plate - WT; PN UDI1001 10X Genomics; Dual Index Kit TS, Set A; PN 1000251 Illumina; IDT for Illumina - TruSeq RNA UD Indexes v2 (96 Indexes, 96 Samples); PN 20040871 10X Genomics; Single Index Kit N, Set A (96 rxn); PN 1000212 10X Genomics; Dual Index Kit TT, Set A (96 rxn); PN 1000215 |
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| Sample indexing set * | The specific sequencing barcode index set used, chosen from the sample indexing kit. For example, in the case of 10x, this might be “SI-GA-A1”, whereas for Nextera, it could be “N505 - CTCCTTAC”. Example: SI-GA-A1 | ||
| Is technical replicate? * | Indicates whether the sequencing reaction was run in replicate. If “Yes,” the corresponding FASTQ files in the dataset should be merged for analysis. Example: Yes | Yes No |
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| Expected entity capture count | The number of cells, nuclei, or capture spots expected to be captured by the assay. Example: 14336 | ||
| Sequencing reagent kit * | The reagent kit used to perform the sequencing run, including the platform, kit name, version, and product number when available. If the reagent kit was prepared using a custom protocol, enter “Custom”. Example: Illumina; NextSeq 500/550 Hi Output Kit 150 Cycles; v2.5; PN 20024907 | Illumina; NovaSeq X Series 25B Reagent Kit (100 Cycle); PN 20125967 Illumina; NextSeq 1000/2000 P2 XLEAP-SBS Reagent Kit (100 Cycles); PN 20100987 Illumina; NovaSeq X Series 1.5B Reagent Kit (300 Cycle); PN 20104705 Illumina; NovaSeq 6000 S1 Reagent v1.5 Kit (200 Cycles); PN 20028318 Illumina; NextSeq 2000 P3 Reagent Kit (300 Cycles); PN 20040561 Illumina; NextSeq 1000/2000 P2 Reagent v3 Kit (200 Cycles); PN 20046812 Illumina; NovaSeq X Series 25B Reagent Kit (200 Cycle); PN 20125968 Illumina; NovaSeq X Series 1.5B Reagent Kit (200 Cycle); PN 20104704 Illumina; NovaSeq X Series 10B Reagent Kit (300 Cycle); PN 20085594 Element Biosciences; AVITI 2x75 Sequencing Kit Cloudbreak FS High Output (184 Cycles); PN 860-00015 Illumina; NovaSeq X Series 1.5B Reagent Kit (100 Cycle); PN 20104703 Illumina; NovaSeq X Series 25B Reagent Kit (300 Cycle); PN 20104706 Illumina; NovaSeq 6000 S1 Reagent v1.5 Kit (100 Cycles); PN 20028319 Illumina; NovaSeq 6000 SP Reagent v1.5 Kit (100 Cycles); PN 20028401 Illumina; HiSeq 3000/4000 PE Cluster Kit PE-410-1001; PN 1000283 Illumina; NextSeq 1000/2000 P2 Reagent v3 Kit (300 Cycles); PN 20046813 Illumina; NextSeq 2000 P3 Reagents Kit (100 Cycles); PN 20040559 Illumina; NovaSeq 6000 S1 Reagent v1.5 Kit (300 Cycles); PN 20028317 Custom Illumina; NextSeq 1000/2000 P2 Reagent v3 Kit (100 Cycles); PN 20046811 Illumina; NovaSeq X Series 10B Reagent Kit (200 Cycle); PN 20085595 Illumina; NextSeq 500/550 Hi Output Kit 150 Cycles; v2.5; PN 20024907 Illumina; NextSeq 500/550 Mid Output Kit 150 Cycles v2.5; PN 20024904 Illumina; NovaSeq 6000 S1 Reagent Kit (200 Cycles); PN 20012864 Illumina; NovaSeq 6000 SP Reagent v1.5 Kit (300 Cycles); PN 20028400 Illumina; NovaSeq 6000 S4 Reagent v1.5 Kit (200 Cycles); PN 20028313 Illumina; NextSeq 500/550 Hi Output Kit 75 Cycles v2.5; PN 20024906 Illumina; NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles); PN 20028312 Illumina; NovaSeq X Series 10B Reagent Kit (100 Cycle); PN 20085596 Illumina; NovaSeq 6000 S2 Reagent v1.5 Kit (100 Cycles); PN 20028316 |
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| Sequencing read format * | The number of sequencing cycles for each read and index in the sequencing run, typically reported as a comma-separated list in the order: Read 1, i7 index, i5 index, and Read 2. Example: 50,8,16,50 | ||
| Sequencing batch ID | A lab-assigned identifier for the sequencing run, such as a chip ID, that enables users to determine which samples were processed together. To avoid ID conflicts across institutions, it is recommended to prefix the ID with the name of the sequencing center. Example: Broad_Chip1234 | ||
| Capture batch ID | A lab-assigned identifier used to indicate which cells or nuclei were captured together in the same run. For example, in a 10X Genomics Chromium Controller workflow, this could be the chip ID used to trace datasets derived from a single capture event. This ID helps users identify samples processed together on the same device. To avoid conflicts across institutions, it is recommended to prefix the ID with the name of the sequencing center. Example: Broad_Batch1234 | ||
| Preparation instrument vendor | The company that manufactures the instrument used to prepare the sample (e.g., for staining or other processing steps) prior to the assay. If the instrument was custom-built or developed internally, enter “In-House”. If no sample preparation occurred, enter “Not applicable”. Example: 10X Genomics | In-House Leica Biosystems Not applicable Thermo Fisher Scientific Roche Diagnostics HTX Technologies 10x Genomics Hamamatsu Ionpath Akoya Biosciences SunChrom |
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| Preparation instrument model | The specific model of the instrument used for sample preparation, such as staining. Manufacturers may offer multiple models with varying features or sensitivities, which can influence how the sample is processed and how the resulting data is interpreted. If no sample preparation occurred, enter “Not applicable”. Example: Chromium X | NanoZoomer S210 Not applicable Sublimator EVOS M7000 Chromium Controller Custom NanoZoomer S360 NanoZoomer S60 Chromium X AutoStainer XL Visium CytAssist SunCollect Sprayer M3+ Sprayer Discovery Ultra ST5020 Multistainer Chromium iX Chromium Connect M5 Sprayer TM-Sprayer |
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| Preparation instrument kit | The reagent kit used in conjunction with the preparation instrument for sample processing (e.g., staining or labeling). If the reagent kit was prepared using a custom protocol, enter “Custom”. Example: 10X Genomics; Chromium Next GEM Chip G Single Cell Kit, 48 rxns; PN 1000120 | 10X Genomics; Chromium Next GEM Chip K Automated Single Cell Kit, 48 rxns; PN 1000289 10x Genomics; Chromium Single Cell B Chip Kit, 16 rxns; PN 1000074 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 4 rxn; PN 1000285 10x Genomics; Visium HD, Mouse Transcriptome, 6.5 mm, 4 runs; PN 1000676 10X Genomics; Chromium Next GEM Chip K Single Cell Kit, 48 rxns; PN 1000286 10x Genomics; Chromium Chip E Single Cell ATAC Kit, 48 rxns; PN 1000155 10X Genomics; Chromium Next GEM Chip G Single Cell Kit, 48 rxns; PN 1000120 Custom 10x Genomics; Visium HD, Human Transcriptome, 6.5 mm, 4 runs; PN 1000675 10X Genomics; Chromium Next GEM Chip K Single Cell Kit, 16 rxns; PN 1000287 10x Genomics; Chromium Next GEM Chip J Single Cell Kit, 16 rxns; PN 1000230 10X Genomics; Chromium Next GEM Chip G Single Cell Kit, 16 rxns; PN 1000127 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 16 rxn; PN 1000283 10x Genomics; Visium HD, Human Transcriptome, 11 mm, 4 runs; PN 1001037 10x Genomics; Chromium GEM-X Single Cell 3' Kit v4, 16 rxns; PN 1000691 10X Genomics; Visium FFPE Reagent Kit v2-Small, PN 1000436 10X Genomics; Chromium Next GEM Chip Q Single Cell Kit, 16 rxns; PN 1000422 10x Genomics; Chromium Next GEM Chip J Single Cell Kit, 48 rxns; PN 1000234 10x Genomics; GEM-X Flex Gene Expression Chip Kit, 4 chips; PN 1000791 10x Genomics; Visium HD, Mouse Transcriptome, 11 mm, 4 runs; PN 1001038 |
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| Metadata schema ID * | The unique string identifier for the metadata specification version, which is easily interpretable by computers for purposes of data validation and processing. Example: 22bc762a-5020-419d-b170-24253ed9e8d9 |
Definitions
| Pattern | Required? | Description |
|---|---|---|
| extras/expected_cell_count.txt$ | The expected cell count for the RNA sequencing dataset. This is an optional file that, if present, will be used by the HIVE’s RNA sequencing analysis pipeline. With some datasets, knowing the expected cell count has improved the output of the HIVE analysis pipeline. | |
| raw/ | ✓ | All raw data files for the experiment. |
| raw/fastq/ | ✓ | Raw sequencing files for the experiment. |
| raw/fastq/RNA/ | ✓ | Directory containing fastq files pertaining to RNAseq sequencing. |
| raw/fastq/RNA/*_R*.fastq.gz$ | ✓ | This is a GZip’d version of the forward and reverse fastq files from RNAseq sequencing (R1 and R2). |
| lab_processed/ | Experiment files that were processed by the lab generating the data. |
Example
. ├── extras/ │ └── expected_cell_count.txt ├── raw/ │ └── fastq/ | └── RNA/ | └── foobar_R01.fastq.gz └── lab_processed/
HuBMAP Datasets
The HuBMAP Data Portal is an open platform to discover, visualize, and download standardized healthy single-cell and spatial tissue data.
View HuBMAP Datasets
Citation
Fisher SA, Hardi J, Morgan R, Nordgren E, Kant PM, Honick B, Rosario J, O'Connor MJ, Turner ML, DCWG Members, Gehlenborg N, Blood PD, Silverstein JC, Musen MA. 2026. The HuBMAP Framework for Advancing Data FAIRness. submitted. https://doi.org/10.64898/2026.06.01.728946
Reuse
This standard may be reused, expanded, or referenced by external repositories. It represents version 5.0 of the metadata schema and version 2.1 of the file directory structure. If you are uploading data, the latest versions of both are available at the "Latest Versions" link below. If you are querying or using HuBMAP or SenNet metadata, a harmonized view of the metadata specification is available at the "Harmonized Version" link below.
Latest Versions Harmonized Version
Contributors
Below is the information for the individuals who contributed to the HuBMAP and SenNet metadata reporting standards.
For questions about this standard, email HuBMAP Helpdesk. You can alternatively reach out to the individuals listed below, either via the email address listed in the table or via contact information provided on their ORCID profile page.
| Name | Affiliation | Contact | ORCID |
|---|---|---|---|
| Stephen A Fisher | University of Pennsylvania, Philadelphia PA, USA | safisher@upenn.edu | 0000-0001-8034-7685 |
| Josef Hardi | Stanford University, Stanford, CA, USA | johardi@stanford.edu | 0000-0002-2533-6681 |
| Richard Morgan | University of Pittsburgh, Pittsburgh, PA, USA | rsm66@pitt.edu | 0009-0003-1800-8545 |
| Mark A Musen | Stanford University, Stanford, CA, USA | musen@stanford.edu | 0000-0003-3325-793X |
| Jonathan C Silverstein | University of Pittsburgh, Pittsburgh, PA, USA | j.c.s@pitt.edu | 0000-0002-9252-6039 |
| Erik Nordgren | University of Pennsylvania, Philadelphia PA, USA | 0000-0002-5024-0278 | |
| Peter M Kant | University of Pittsburgh, Pittsburgh, PA, USA; Currently - Otsuka Precision Health, Princeton, NJ, USA | 0009-0002-6510-5041 | |
| Brendan John Honick | Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA | 0000-0001-6128-9854 | |
| Martin J O’Connor | Stanford University, Stanford, CA, USA | 0000-0002-2256-2421 | |
| Jean G Rosario | University of Pennsylvania, Philadelphia PA, USA | 0000-0002-6116-5058 | |
| Nils Gehlenborg | Harvard Medical School, Boston, MA, USA | 0000-0003-0327-8297 | |
| Philip D Blood | Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA | 0000-0002-9129-1223 | |
| Kyung Jin Ahn | Children’s Hospital of Philadelphia, Philadelphia, PA, USA | 0000-0002-4184-482X | |
| Christopher R Anderton | Pacific Northwest National Laboratory, Richland, WA, USA | 0000-0002-6170-1033 | |
| Shovik Bandyopadhyay | Children’s Hospital of Philadelphia, Philadelphia, PA, USA; Brigham and Women’s Hospital, Boston, MA, USA | 0000-0003-3919-3914 | |
| Kenneth C Bedi | University of Pennsylvania, Philadelphia PA, USA | 0000-0003-3588-9324 | |
| Maigan Brusko | University of Florida, Gainesville, FL, USA | 0000-0002-4331-2202 | |
| Martha Campbell-Thompson | University of Florida, Gainesville, FL, USA | 0000-0001-6878-1235 | |
| James Carson | The University of Texas at Austin, Austin, TX, USA | 0000-0001-9009-5645 | |
| Chase M Carver | Mayo Clinic, Rochester, MN, USA | 0000-0003-4002-2418 | |
| Jing Chen | University of Florida, Gainesville, FL, USA | 0000-0001-8008-8062 | |
| Anthony M Corbett | University of Rochester Medical Center, Rochester, NY, USA | 0000-0001-9545-0853 | |
| Alexandra E Cuaycal | University of Florida, Gainesville, FL, USA | 0000-0002-9060-6326 | |
| Penny Cuda | Carnegie Mellon University, Pittsburgh, PA, USA | 0009-0002-6547-2650 | |
| Dinh Diep | University of California, San Diego, CA, USA; Currently - Altos Labs, San Diego, CA, USA | 0000-0001-6057-4119 | |
| Sergii Domanskyi | The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA | 0000-0002-6847-6019 | |
| Sean Donahue | Carnegie Mellon University, Pittsburgh, PA, USA | 0000-0002-4072-2046 | |
| Michael P Duffy | University of Pennsylvania, Philadelphia PA, USA | 0000-0001-5325-3683 | |
| Michael T Eadon | Indiana University School of Medicine, Indianapolis, IN, USA | 0000-0003-3066-2876 | |
| Jean Fan | Johns Hopkins University, Baltimore, MD, USA | 0000-0002-0212-5451 | |
| Melissa A Farrow | Vanderbilt University, Nashville, TN, USA | 0000-0002-1602-2082 | |
| Kathleen M Fisch | University of California San Diego, La Jolla, CA, USA | 0000-0002-0117-7444 | |
| William F Flynn | The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA | 0000-0001-6533-0340 | |
| James M Fulcher | Pacific Northwest National Laboratory, Richland, WA, USA | 0000-0001-9033-3623 | |
| Soumya Ghose | GE HealthCare, Niskayuna, NY, USA | 0000-0002-2730-1482 | |
| Fiona Ginty | GE HealthCare, Niskayuna, NY, USA | 0000-0001-6638-683X | |
| Joana P Gonçalves | Delft University of Technology, Delft, The Netherlands | 0000-0001-6072-9627 | |
| Yongqun He | University of Michigan, Ann Arbor, MI, USA | 0000-0001-9189-9661 | |
| Po Hu | Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA | 0000-0003-2422-1652 | |
| Sanjay Jain | Washington University School of Medicine, St. Louis, MO, USA | 0000-0003-2804-127X | |
| Thomas V Karathanos | Stanford University, Stanford, CA, USA | 0000-0003-1754-3872 | |
| Madhurima Kaushal | Washington University School of Medicine | 0000-0003-2760-0586 | |
| Angela RS Kruse | Vanderbilt University, Nashville, TN, USA; Currently - The Ohio State University, Columbus, OH, USA | 0000-0001-8776-2769 | |
| Yumi Kwon | Pacific Northwest National Laboratory, Richland, WA, USA | 0000-0003-0523-6197 | |
| Blue B Lake | University of California San Diego, La Jolla, CA, USA; Currently - Altos Labs, San Diego, CA, USA | 0000-0002-8637-9044 | |
| Roy Lardenoije | Delft University of Technology, Delft, The Netherlands | 0000-0002-9026-7870 | |
| Shin Lin | Emory University, Atlanta, GA, USA | 0000-0003-0118-0413 | |
| Yiing Lin | Washington University, St. Louis, MO, USA | 0000-0002-0317-7608 | |
| Scott A Lindsay | University of California, San Diego, CA, USA | 0000-0002-2929-7755 | |
| Peiran Lu | Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA | 0009-0001-5096-3046 | |
| Clayton Mathews | University of Florida, Gainesville, FL, USA | 0000-0002-8817-6355 | |
| Elizabeth McDonough | GE HealthCare, Niskayuna, NY, USA | 0000-0001-7524-8260 | |
| Ricardo Melo Ferreira | Indiana University School of Medicine, Indianapolis, IN, USA | 0000-0003-2063-9744 | |
| Emma M Monte | Stanford University, Stanford, CA, USA | 0000-0003-2566-1967 | |
| Kathleen O’Neill | University of Pennsylvania, Philadelphia PA, USA | 0000-0003-1980-6840 | |
| Minxing Pang | University of Pennsylvania, Philadelphia PA, USA | 0000-0001-5208-5972 | |
| Mana Parast | University of California San Diego, La Jolla, CA, USA | 0000-0001-5963-2246 | |
| Liming Pei | Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA | 0000-0002-1924-0333 | |
| Samuel Peters | University of Minnesota, Minneapolis, MN, USA | 0000-0003-1479-8087 | |
| Ajay Pillai | National Institute of Health, Bethesda, MD, USA | 0000-0002-9789-7189 | |
| Gloria Pryhuber | University of Rochester Medical Center, Rochester, NY, USA | 0000-0002-9185-3994 | |
| Ling Qin | University of Pennsylvania, Philadelphia PA, USA | 0000-0002-2582-0078 | |
| Presha Rajbhandari | Columbia University, NYC, NY, USA | 0000-0003-2184-7238 | |
| Matthew M Ruffalo | Carnegie Mellon University, Pittsburgh, PA, USA | 0000-0003-2222-6169 | |
| Pinaki Sarder | University of Florida, Gainesville, FL, USA | 0000-0003-2450-5233 | |
| Diane C Saunders | Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA; Northwestern University, Chicago, IL, USA | 0000-0002-8849-6746 | |
| Kevin Schneider | Buck Institute, Novato, CA, USA | 0009-0001-8046-0167 | |
| Lingyan Shi | University of California San Diego, La Jolla, CA, USA | 0000-0003-1373-3206 | |
| Santhosh Sivajothi | The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA | 0000-0002-8854-4517 | |
| David Smith | Children’s Hospital of Philadelphia, Philadelphia, PA, USA | 0000-0001-7858-3785 | |
| Jeff M Spraggins | Vanderbilt University, Nashville, TN, USA | 0000-0001-9198-5498 | |
| Valentina Stanley | University of California, San Diego, CA, USA | 0000-0002-2212-7796 | |
| Kai Tan | Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA | 0000-0002-9104-5567 | |
| Anusha Thadi | Children’s Hospital of Philadelphia, Philadelphia, PA, USA | 0000-0002-1271-0398 | |
| Hua Tian | Columbia University Medical Center, NYC, NY, USA | 0000-0002-3598-0219 | |
| Morgan L Turner | Harvard Medical School, Boston, MA, USA | 0000-0002-1512-9742 | |
| Ioannis S Vlachos | Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Cancer Center, Boston, MA, USA; Broad Institute of MIT and Harvard, Boston, MA, USA | 0000-0002-8849-808X | |
| Seth Winfree | University of Nebraska Medical Center, Omaha, NE, USA; Currently - QCDx Inc, Farmington, CT, USA | ||
| Pei-Hsun Wu | Johns Hopkins University, Baltimore, MD, USA | 0000-0002-7371-2960 | |
| Kevin J Zemaitis | Pacific Northwest National Laboratory, Richland, WA, USA | 0000-0002-3524-9776 | |
| Mowei Zhou | Pacific Northwest National Laboratory, Richland, WA, USA; Currently - Zhejiang University, Hangzhou, Zhejiang 310058, China | 0000-0003-3575-3224 | |
| Chenchen Zhu | Department of Genetics, Stanford University, Stanford, CA, USA | 0000-0003-2165-9456 |